The Graph Matching Optimization methodology for thin object recognition in pick and place tasks

Bin-picking emerges as a major interest in the industry. The aim is to replace current ‘pick and place’ systems, where one must place mechanical components in dedicated distribution devices such as bowl feeders for picking them up with a robot arm. A large number of image processing methods are available for recognizing these components. For instance, the stereovision approach provides fine results by comparing several images of the objects taken from different angles. However, when several types of components are available or for thin components, the identification remains a delicate task. We propose the Graph Matching Optimization methodology, which uses graph comparison with evolutionary algorithms between stereoscopic images and a model, in order to identify thin pieces in a constrained time frame. First, we extract characteristic component information by binarization and skeletonization of the images. Then, we retrieve the position of the objects in a 3 three-dimensional space through an evolutionary algorithm derived from Harmony Search Optimisation (HSO). Lastly, we extract and validate optimal parameter ranges for which the devised algorithm shows a high efficiency for representative component positions of randomly arranged thin objects.